PODPAC

Pipeline for Observational Data Processing Analysis and Collaboration

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PODPAC is a python library that builds on the scientific python ecosystem to enable simple, reproducible geospatial analyses that run locally or in the cloud.

import podpac

# elevation
elevation = podpac.data.Rasterio(source="elevation.tif")

# soil moisture
soil_moisture = podpac.data.H5PY(source="smap.h5", interpolation="bilinear")

# evaluate soil moisture at the coordinates of the elevation data
output = soil_moisture.eval(elevation.coordinates)

# run evaluation in the cloud
aws_node = podpac.managers.aws.Lambda(source=soil_moisture)
output = aws_node.eval(elevation.coordinates)

Elevation (left), Soil Moisture (center), Soil Moisture at Elevation coordinates (right).

Purpose

Data wrangling and processing of geospatial data should be seamless so that earth scientists can focus on science. The purpose of PODPAC is to facilitate:

• Access of data products

• Subsetting of data products

• Projecting and interpolating data products

• Combining/compositing data products

• Analysis of data products

• Sharing of algorithms and data products

• Use of cloud computing architectures (AWS) for processing

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Getting Started

• Overview
• Installation
• Settings
• Examples
• AWS Integration

Topics

• Why PODPAC?
• Design
• Dependencies
• Nodes
• Coordinates
• Cache
• Supported Datasets
• Interpolation
• NASA Earth Data Login
• AWS Development

References

• API Reference
• Wrapping Datasets

Support

• References
• Contributing
• Documentation
• Development Roadmap
• Changelog

Acknowledgments

This material is based upon work supported by NASA under Contract No 80NSSC18C0061.